12 th World Telecommunication/ICT Indicators Symposium (WTIS-14)
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1 12 th World Telecommunication/ICT Indicators Symposium (WTIS-14) Tbilisi, Georgia, November 2014 Presentation Document C/19-E 25 November 2014 English SOURCE: TITLE: Ministry of Internal Affairs and Communications, Japan Efforts for Big Data and Open Data in Japan
2 Efforts for Big Data and Open Data in Japan Director General for International Affairs, Global ICT Strategy Bureau, MIC, Japan MORI Kiyoshi 25 Nov.2014 Arrival of data utilization society Rapid evolution of ICT Many kinds and large amounts of data is generated, distributed and accumulated can be easily handled by companies etc. Many kinds and large amounts of digital data exist in society, market, etc. Utilizing creation of new value resolution of social issues Utilizing of the data is important 1 Big Data 2 Open Data Open up public data owned by the nation and local public bodies to the private sector in formats suitable for secondary use. Utilizing Creation of new businesses and services Realization of public services by public-private collaboration 1
3 "Data quality" in accordance with big data and open data "Data quality" is a critical issue. "Data quality" Ex. ( ビッグデータ オープンデータ ) Importance Accuracy, reliability Consistency, compatible potential 1 Big Data 2 Open Data There is a wide variety of data, so it is difficult to grasp the current situation. Accuracy and reliability of the data itself is high in many cases. However, data of various fields exists in various data formats. First, one of the issues is to measure the type and amount of data. The important issues are standardization of data formats, rules for secondary use and API. Measurement of distribution and accumulation amount of big data human resources/organization [data scientist etc.] analytical technology [machine learning, statistical analysis etc.] broad sense narrow sense unstructured data (new) Blog/SNS, sensor log, access log etc. unstructured data (old) voice, radio, TV, newspaper, book etc. structured data customer data, sales data etc. No corresponding statistics. Corresponding statistics exist. 2
4 (Ref.) The targeted media for estimation of Big Data distribution Structured data Unstructured data Business system Web service (EC, etc) Customer DB Medical receipt data Sales log in e-commerce POS data (basket data at the point of sale) Accounting data Business diary (text) Medical electronic health record (text) Medical diagnostic imaging (image, still image, movie) CTI voice log data (voice) Sensor GPS M2M GPS data Weather data RFID data Sensor log Traffic-congestion Security and remote monitoring camera Media content Video and image viewing log Personal media social media Blog, SNS articles etc (text) Access log (various logs) Mobile phone [including PHS,voice] (text) Fixed IP phone[voice] Estimation results: the volume of Big Data distribution in Japan (TB) 14'000'000 13,516,492 12'000'000 10'000'000 About an 8.7-fold increase in eight years 8'000'000 8'020'140 6'000'000 4'913'064 6'050'339 4'000'000 3'477'480 4'076'772 2'000'000 1'556'589 2'004'730 2'614' (year) 3
5 Promotion of Open Data: Demonstration Experiments The MIC is conducting a demonstration experiment for environmental improvement related to open data. We aim to (1) establish the common API (Application Programming Interface), (2) develop rules for secondary use of data, (3) visualize the benefits of open data. Disaster prevention service Flood hazard area Evacuation advisory areas Real-time location Delay Public transportation service Ground service Summarize ground of national, prefectural, and local governments Combination of various Common API for circulation and sharing platform Local government Social capital Tourism Disaster prevention Public transportation Statistics Hay fever Three data areas Open Data Big Data Personal Data 4
6 Challenge of data utilization 1. Utilization of personal data (Enterprises) High value of data related directly to users (Consumers) Privacy infringement, concerns of exploitation by a third party A balance between protection and utilization of personal data is important. 2. Data scientist training Securing human resources who derive useful knowledge from data and use it to solve problems is important. However, there are few such human resources ("Data Scientist ). Data scientist training is important. Thank you for your attention. 5
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